Location

Duration

Learn to carve valuable information from your masses of data!

Our Python Data Analysis course for Data Scientists covers an
introduction to the core concepts of the Python language, ultimately
focusing on Big Data Analytics including how to best manipulate and visualise your data with Python's excellent library support.

The
course is intensive and is intended for Data Scientists, Data Analysts,
and Business Intelligence experts who want to understand how to use
Python in their data-oriented environment, as well as Python developers
who want to get introduced to Data Science.

Practical exercises and interactive walk-throughs are used throughout, so attendees have the opportunity
to apply the proposed concepts on real Data Science applications, from
exploratory data analysis to predictive analytics.

Prerequisites

On-site

We would be happy to discuss custom / on-site Python Data Science training for any size of team. We can take into account your existing technical skills, project requirements and timeframes, and specific topics of interest to tailor the most relevant and focussed course for you.

This can be particularly useful if you need to learn just the new features and Python programming Best Practices, or need to include extra topics to help with pre-requisite skills. If you would like to dicuss your custom training requirements, please get in touch.

Python Setup (Installation & Packaging)

Installation, packaging and virtualisation of Python using Conda.

We'll set up Python using the Anaconda distribution, a free and enterprise-ready Python distributionthat includes hundreds of the most popular Python packages for science, math, engineering and dataanalysis. Anaconda comes with Conda, a cross-platform tool for managing packages and virtual environments. We'll also set up Jupyter, a web-based interactive environment where users can organise, write and run their Python code in notebooks.

Python Core Concepts and Best Practices

Overview of how Python is used for Data Science and Data Analytics projects.Notions of Object-Oriented Programming and Functional Programming, applied to the design of Python applications and analysis pipelines using best practices.

Python Data Science Tools

We'll explore the most important Python tools for Data Science.

NumPy, short for Numerical Python, is one of the main building blocks for scientific computing in Python.
It provides high speed manipulation of multi-dimensional arrays and
it's used by higherlevel libraries (like pandas) to support
sophisticated analytics with high speed computation.

pandasis a highly performant library for data manipulation and data analysis in Python.It's built on top of NumPy and optimised for performance, while offering a high-level interface.We'll discuss how to create and manipulate Series and DataFrame objects in pandas, accessing data from multiple sources, cleaning and transforming data sets to get them in the right shape for advanced analysis.

Accessing and Preparing Data

Data
can come in multiple formats and from multiple sources. We'll examine
how to read and write data from local files in different formats, and
how to access data from remote source.

Data cleaning and data preparation are the first steps in a data analysis project, so we'll discuss how to perform data transformation to get ready for further analysis.

Data Analysis

With our data in the right shape, we're ready to analyse them in order to extract useful insights. We'll perform the computation of summary information and basic statistics from data sets.

Data Visualisation with Python

Data analysis benefits from the visualisation of data.

If a picture if worth a thousand words, complex data structures can be easier to understand and analyse using effective visualisation techniques. Communicating the results with non-technical users is also a challenge that visualisation techniques help to overcome.

"The pace of the course and the instructor's flexibility meant we were able to cover a lot of ground, all of which was directly relevant to our upcoming projects. A number of different Big Data solutions were explored."